Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Центральность по близости× | Анализ сетевой диффузии× | |
|---|---|---|
| Область | Сетевой анализ | Сетевой анализ |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1950 (formalized 1979) | 1927 (epidemic roots); network formalization 1990s–2000s |
| Автор метода≠ | Bavelas, A.; formalized by Freeman, L. C. | Kermack, W. O. & McKendrick, A. G. |
| Тип≠ | Node-level centrality index | Simulation / analytical model |
| Основополагающий источник≠ | Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239. DOI ↗ | Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721. DOI ↗ |
| Другие названия | closeness, farness-based centrality, geodesic closeness, normalized closeness centrality | diffusion on networks, information diffusion, contagion spreading model, network propagation model |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Closeness centrality measures how quickly a node can reach all others in a network by computing the inverse of its average shortest-path distance to every other node. First described by Bavelas (1950) and formally unified by Freeman (1979), it identifies nodes that can spread information or resources efficiently across the entire graph — not merely nodes with many direct contacts. | Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally. |
| ScholarGateНабор данных ↗ |
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